Coronary Artery Disease, Arterial Stiffness, Blood Pressure, AORTIC VALVE DISEASES, Mitral Valve Stenosis and/or Insufficiency, Heart Failure, Pulmonary Disease, Chronic Obstructive (COPD), Steatohepatitis, Inflammation Biomarkers, Lipid Profile, Oral Microbiota, MASLD - Metabolic Dysfunction-Associated Steatotic Liver Disease, Metabolic Associated-dysfunction Steatohepatitis (MASH)
Conditions
Keywords
coronary artery disease, arterial stiffness, MASLD, COPD, inflammation, dyslipidemia, aortic aneurysm, oral microbiota
Brief summary
The Swedish CArdioPulmonary bioImage Study (SCAPIS) is a unique, large-scale national research initiative involving 30,000 randomly selected individuals aged 50-64, recruited between 2014 and 2018. The study is a collaborative effort among six university hospitals across Sweden. A follow-up study, SCAPIS 2, is conducted for half of the original participants. In Stockholm, 2,500 individuals will be re-examined at Danderyd University Hospital and Karolinska Institutet. SCAPIS 2 includes a core set of examinations involving blood sampling, questionnaires, and imaging. In addition to these, complementary local investigations are conducted to enable more detailed research questions. This protocol describes the additional studies conducted in the Stockholm cohort. All complementary assessments aim to identify risk factors for current and future lung, liver, and cardiovascular diseases.: EXTENDED SAMPLING: Saliva and Blood Samples with Blood Cell Isolation. EXTENDED QUESTIONNAIRES: Dyspnea, Sleep, Respiratory Infections, and Dental Health. EXTENDED IMAGING AND PHYSIOLOGICAL MEASUREMENTS Cardiac Ultrasound and Abdominal Aortic Measurements. Liver Elastography. Vascular Stiffness by cuff-based pulse wave analysis and Photoplethysmography (PPG). Valvular and Vascular Calcification by CT imaging.
Detailed description
The Swedish CArdioPulmonary bioImage Study (SCAPIS) is a unique, large-scale national research initiative involving 30,000 randomly selected individuals aged 50-64, recruited between 2014 and 2018. The study is a collaborative effort among six university hospitals across Sweden. A follow-up study, SCAPIS 2, is conducted for half of the original participants. In Stockholm, 2,500 individuals will be re-examined at Danderyd University Hospital and Karolinska Institutet. SCAPIS 2 includes a core set of examinations involving blood sampling, questionnaires, and imaging. In addition to these, complementary local investigations are conducted to enable more detailed research questions. This protocol describes the additional studies conducted in the Stockholm cohort: EXTENDED SAMPLING: Saliva and Blood Samples with Blood Cell Isolation Additional blood samples will be collected to isolate blood cells for detailed immune system profiling, red blood cell function nd for lipid profiles and inflammatory markers. Saliva samples will be analyzed for inflammatory markers and microbial composition. EXTENDED QUESTIONNAIRES: Dyspnea, Sleep, Respiratory Infections, and Dental Health Participants will complete detailed questionnaires covering recent food, drink, and medication intake; oral hygiene; breathing and lung function; sleep quality and daytime somnolence; and history of respiratory infections, including COVID-19. EXTENDED IMAGING AND PHYSIOLOGICAL MEASUREMENTS: These investigations will be conducted during a separate visit, coordinated with the main study, and take approximately one hour. 1. Cardiac Ultrasound and Abdominal Aortic Measurements: cardiac structure and function, as well as the diameter of major arteries (e.g., the thoracic and abdominal aorta). 2. Liver Elastography: An ultrasound-based technique used to assess liver stiffness and fat content. 3. Vascular Stiffness: Blood pressure cuff-based pulse wave analysis with automated stiffness calculation. Photoplethysmography (PPG) using skin-mounted sensors (commonly used for oxygen saturation monitoring, in cuffless blood pressure monitors and wearables). Single-lead ECG via adhesive electrodes. 4. Valvular and Vascular Calcification: In the main SCAPIS study, CT imaging of the heart and large vessels will be performed. In SCAPIS 2, images will be re-analyzed using algorithms that quantify calcium deposits in heart valves. RESEARCH QUESTIONS Blood and blood cell analyses will be conducted to assess immune activation, lipids and lipid mediators, and immune cell phenotypes. These analyses aim to distinguish chronic from self-limiting cardiovascular inflammation and to detect prevalent, incident, and subclinical cardiovascular disease (CVD) for improved risk assessment. Saliva biomarkers, with a focus on inflammation and oral microbiota, will be studied to explore links between oral health, respiratory health, systemic inflammation, and comorbidities. Survey data on sleep, breathing, respiratory infections, and oral health will be used to investigate associations with liver, lung, and cardiovascular diseases, including their risk factors and prognosis. The prevalence of liver steatosis and fibrosis in the general population wil be examined, along with their associations with CVD and heart failure. It will also be assess whether SCAPIS data can predict liver stiffness. Risk factors for cardiac dysfunction and valvular disease, as well as progression to heart failure will be analyzed. CT-based calcium scoring in the aorta and valves will be studied for associations to valve disease, heart failure, intervention timing, and progression of aortic dilation between SCAPIS 1 and 2. Aortic aneurysm prevalence and risk factors will be evaluated, including associations with SCAPIS variables and incident cardiovascular disease. Infrarenal aortic diameter is also used as a subclinical marker of aortic aneurysm. New techniques using photoplethysmography (PPG) for arterial stiffness and pulse characteristics will be studied in relation to coronary artery disease, cardiac structure and function, valve disease, liver stiffness, biomarkers, and blood pressure. Finally, SCAPIS and SCAPIS 2 data will be integrated including substudy results for comprehensive risk assessment of clinical and subclinical CVD and to guide prevention strategies.
Interventions
DIAGNOSTIC_TESTCoronary computer tomography (CCTA)
Siemens Naeotom Alpha Photon Counting CT scanner
DIAGNOSTIC_TESTEchocardiograhy and aortic ultrasound
Echocardiograhy and aortic ultrasound using General Electric Vivid E95
DIAGNOSTIC_TESTLiver elastography
Liver elastography using Fibroscan ultrasound method.
DIAGNOSTIC_TESTArterial stiffness
Arterial stiffness using Arteriograph, Tensiomed, Hungary and Photoplethysmography and ECG by ADInstrument
Sponsors
Danderyd Hospital
Region of Stockholm
Karolinska Institutet
Göteborg University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
56 Years to 74 Years
Healthy volunteers
Yes
Inclusion criteria
Subjects already included in the main / general SCAPIS reexamination study in Stockholm.
Exclusion criteria
Inability to provide consent.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Diagnostic Performance of Machine Learning Analysis of Finger Photoplethysmography for Detection of Coronary Artery Disease (CAD-RADS ≥2) | Typically within 1 months of enrollment. | Area under the receiver operating characteristic curve (AUC-ROC) for prediction of CAD-RADS ≥2 using machine learning analysis of finger photoplethysmography. CAD-RADS 2.0 classification is based on coronary computed tomography angiography (CCTA), where stenosis severity is graded 0-5, atherosclerotic burden is quantified using segment involvement score (SIS) and calcification using Agatston Units (CAC-score). |
| Long-Term Incident Major Adverse Renal and Cardiovascular Events (MARCE) | 3-8 years | Incidence of major adverse renal and cardiovascular events during follow-up. Associations will be evaluated with baseline immune mediators, lipid mediators, oral microbiota composition, vascular stiffness, steatohepatitis, and fibrosis measures. |
| Immune Activation Biomarkers and Prevalent Atherosclerotic Cardiovascular Disease | Typically within 1 months of enrollment | Correlation between circulating biomarkers of immune activation and the degree of calcification and/or atherosclerosis at examined cardiovascular sites. Atherosclerosis and calcification are assessed by CCTA and/or ultrasound imaging and/or prior diagnosis of atherosclerotic cardiovascular disease. Immune profiling distinguishes resolving and non-resolving inflammatory phenotypes. |
| Lipid Mediator Profiles and Prevalent Atherosclerotic Cardiovascular Disease | Typically within 1 months of enrollment | Correlation between circulating lipid mediator profiles and degree of calcification and/or atherosclerosis at examined cardiovascular sites. Lipid profiling characterizes inflammatory phenotypes and evaluates associations with prior diagnosis of atherosclerotic cardiovascular disease, calcification, and/or imaging-defined atherosclerosis assessed by CCTA and/or ultrasound. |
| Correlation Between Oral Microbiota Composition and Prevalent Coronary Artery Disease | Typically within 1 months of enrollment | Correlation between oral viral, fungal, and bacterial microbiota composition and degree of coronary artery disease as assessed by CCTA (CAD-RADS, SIS and CAC-score). |
| Prevalence of Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) | Typically within 1 months of enrollment | Prevalence of increased estimated hepatic lipid content defined as controlled attenuation parameter (CAP) \>285 dB/m measured by transient elastography (FibroScan). |
| Prevalence of Abdominal Aortic Aneurysm | Typically within 1 months of enrolment | Prevalence of abdominal aortic aneurysm ≥ 30 mm as determined by abdominal ultrasound imaging. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Multimodal artificial intelligence (AI) model for prediction of coronary artery disease (CAD) | Typically within 1 month of enrollment | Machine learning methods will be applied to identify the most informative predictors of CAD. Multimodal data, including ECG-derived features, lipid mediator measurements, inflammatory biomarkers, ultrasound variables, and oral microbiota profiles, will be used to develop and evaluate a prediction model for CAD, defined as CAD-RADS ≥2. Cross-validation will be used to reduce the risk of overfitting. Model performance will be evaluated using measures of discrimination and calibration. Clinical decision-curve analysis will be used to assess the clinical utility of the model by estimating net benefit across relevant decision thresholds and comparing model-guided decisions with current clinical practice (the pooled cohort equation). |
| Photoplethysmograpy (PPG) to predict prevalent coronary artery disease | Typically within 3 months of enrollment | Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥2 and SIS and CAC-score when adding PPG features to age and sex. |
| Incremental Predictive Value of Photoplethysmography Beyond Traditional Cardiovascular Risk Models | Typically within 1 month of enrollment | Improvement in AUC-ROC for predicting CAD-RADS category, segment involvement score (SIS), and coronary artery calcium (CAC) score when adding machine learning-derived PPG features to traditional cardiovascular risk factors including the Pooled Cohort Equation. |
| Lipid Mediator Profiles and Coronary Artery Disease Severity | SCAPIS baseline and SCAPIS2 re-examination (8-10 years). | Association between circulating lipid mediator profiles including plasma remnant cholesterol and coronary artery disease severity assessed by CCTA using CAD-RADS, SIS and CAC-score. |
| Arterial stiffness | Typically within 3 months of enrollment | Associations between immune mediators, lipid mediators, oral microbiota, and pulse wave velocity by Arteriograph and PPG features of arterial stiffness. |
| Prevalence of valvular disease and aortic calcification | Typically within 1 month of enrollment | Prevalende of mild, -moderate and severe aortic and mitral stenosis and insufficiency as determined by cardiac echo in relation to valvular and aortic calcification. |
| Machine learning to predict aortic and mitral valve disease | Typically within 1 month of enrollment | Machine learning to predict and to identify top predictors for aortic stenosis, insufficiency and mitral stenosis of insufficiency |
| Agreement between CCTA and cardiac doppler ultrasound | Typically within 3 months of enrollment | Agreement between valvular characterisation on CCTA and degree of aortic and mitral stenosis and insufficiency by cardiac ultrasond |
| Photoplethysmography (PPG) and aortic valve disease | Typically within 3 months of enrollment | Finger PPG features will be will be used to develop and evaluate a machine learning prediction model for aortic valvular disease (stenosis or insufficiency) detected on cardiac ultraound. Cross-validation will be used to reduce the risk of overfitting. Model performance will be evaluated using measures of discrimination (Receiver Operating Characteristic) and calibration. |
| Prevalence and predictors of thoracic aortic dilatation | Typically within 3 months of enrollment | The prevalence of thoracic aortic dilatation (\>39 mm) will be assessed in the study population using coronary CT angiography (CCTA). Available multimodal data, including clinical characteristics, ECG-derived features, physiological measurements, laboratory biomarkers, and imaging variables, will be used to identify independent predictors of thoracic aortic dilatation and to develop prediction models. Model development will include cross-validation to reduce the risk of overfitting. Model performance will be evaluated using measures of discrimination (Receiver Operating Characteristics) and calibration. |
| Left ventricular mass and hypertension | Typically within 3 months of enrollment | Prevalence and associations between blood pressure, hypertension diagnosis and left ventricular mass index |
| Left ventricular and left atrial strain | Typically within 3 months of enrollment | Prevalence and associations between left ventricular and left atrial strain and baseline characteristics |
| Correlation between arterial stiffness and left ventricular hypertrophy | Typically within 3 months of enrollment | Carotid femoral pulse wave velocity (PWV) in relation to left ventricular mass index (LVMI), as assessed by echocardiography. |
| Photoplethysmography and cardiac function | Typically within 3 months of enrollment | Photoplethysmography (PPG)-derived features will be used to develop and evaluate prediction models for cardiac function, including left ventricular ejection fraction (LVEF) and degree of diastolic dysfunction (graded by echocardiography). Machine learning methods will be applied to identify the most informative predictors. Cross-validation will be used to reduce the risk of overfitting. Model performance will be evaluated using measures of (Receiver Operation Caracteristics) and calibration. |
| Prevalence of hepatic fibrosis | Typically within 3 months of enrollment | Prevalence of increased estimated hepatic stiffness defined as ≥8 kPa by liver elastography (Fibroscan) |
| Predictors of hepatic disease | Typically within 3 months of enrollment | Associations between baseline characteristics immune mediators, lipid mediators, oral microbiota, vascular stiffness, and prevalent hepatic disease |
| Prevalence of abdominal aortic subaneurysm | Typically within 3 months of enrollment | Prevalence of abdominal aortic subaneurysm ≥ 25 mm as determined by abdominal ultrasound |
| Predictors and factors associated with infrarenal aortic diameter | Typically within 3 months of enrollment | Prediction of Infrarenal aortic diameter as measured by ultrasound using all available subject data. |
| Association of oral dysbiosis and proteome deregulation with respiratory health, lung imaging, lung function and respiratory symptoms. | Typically, within 12 months of enrolment | Association between changes in indexes in microbiome dysbiosis, increase in selected pathogen bacterial counts and relative abundances, and increase in specific inflammatory protein concentrations and lung health, as presented by symptoms captured by the questionnaires, lung function and lung imaging. |
| Prevalence of oral dysbiosis and proteome deregulation, and their associations with airway diseases such as asthma, chronic obstructive pulmonary disease (COPD), bronchiectasis, and lung parenchymal diseases. | Typically, within 12 months of enrolment | Association between changes in indexes in microbiome dysbiosis, increase in selected pathogen bacterial counts and relative abundances, and increase in specific inflammatory protein concentrations and prevalence and presentation of airway and lung parenchyma diseases |
| Association of oral dysbiosis and proteome deregulation with respiratory health and systemic inflammation | Typically, within 12 months of enrolment | Association between changes in indexes in microbiome dysbiosis, increase in selected pathogen bacterial counts and relative abundances, and increase in specific inflammatory protein concentrations and prevalence of airway and lung parenchyma diseases and the presence of systemic inflammation, as measured by blood inflammatory proteins and urine mediators. |
| Association of oral dysbiosis and proteome deregulation with respiratory health and comorbidities | Typically, within 12 months of enrolment | Association between changes in indexes in microbiome dysbiosis, increase in selected pathogen bacterial counts and relative abundances, and increase in specific inflammatory protein concentrations and prevalence of airway diseases and comorbidities, such as cardiovascular, inflammatory and immune-mediated systemic diseases. |
| Targeted plasma inflammatory markers to predict prevalent coronary artery disease | Typically within 3 months of enrollment | Targeted plasma inflammatory protein panel quantified using Olink Target 48 Cytokine Panels (PEA technology) together with lipid profiling to predict CADRADS, SIS and CAC-score. |
| Gene expression profiling in peripheral blood mononuclear cells (PBMC) and remnant cholesterol | Typically within 3 months of enrollment | Gene expression profiling in peripheral blood mononuclear cells (PBMC) to characterize immune/inflammatory signatures associated with remnant cholesterol levels. |
| Additional value of Plasma Apolipoproteins to Predict Prevalent Coronary Artery Disease | Typically within 2 months of enrollment | Additional valie of plasma levels of ApoB, ApoAI, ApoE, and ApoCIII quantified by commercially available ELISA kits to predict prevalent CAD as CADRADS, SIS- and CAC-score. |
| Prevalence of lipid composition | Typically within 3 months of enrollment | Prevalence of lipoprotein lipid composition (total cholesterol, free cholesterol, cholesteryl esters, triglycerides, phospholipids) and surface/core ratio metrics analyzed by size-exclusion chromatography. |
| Erythrocyte Dysfunction and Coronary Artery Disease | Typically within 3 months of enrollment | Association between measures of erythrocyte function and coronary artery disease severity assessed by CCTA using CAD-RADS, SIS and CAC-score |
| Machine learning of photoplethysmography (PPG) to predict mitral valve disease | Typically within 3 months of enrolment | Finger PPG features will be will be used to develop and evaluate a machine learning prediction model for mitral valv disease (stenosis or insufficiency) detected on cardiac ultraound. Cross-validation will be used to reduce the risk of overfitting. Model performance will be evaluated using measures of discrimination (Receiver Operating Characteristic) and calibration. |
Countries
Sweden
Contacts
CONTACTJonas Spaak, MD, PhD, Professor
PRINCIPAL_INVESTIGATORRebecka Hultgren
Karolinska Institutet
PRINCIPAL_INVESTIGATORKarin Leander
Karolinska Institutet
PRINCIPAL_INVESTIGATORPaolo Parini
Karolinska Institutet
PRINCIPAL_INVESTIGATORDaniel Andersson
Karolinska Institutet
PRINCIPAL_INVESTIGATORMatteo Pedrelli
Karolinska Institutet
PRINCIPAL_INVESTIGATORApostolos Bossios
Karolinska Institutet
PRINCIPAL_INVESTIGATORGeorgios Belibasakis
Karolinska Institutet
PRINCIPAL_INVESTIGATORTomas Jernberg
Karolinska Institutet
PRINCIPAL_INVESTIGATORHannes Hagström
Karolinska Institutet
PRINCIPAL_INVESTIGATORMagnus Bäck
Karolinska Institutet
PRINCIPAL_INVESTIGATORBahira Shahim
Karolinska Institutet
PRINCIPAL_INVESTIGATORZhichao Zhou
Karolinska Institutet
Outcome results
None listed